Relvy AI
Autonomous AI on-call engineer that investigates alerts and creates auditable notebooks.
Relvy is a smart buy for teams flooded with on-call alerts who want a consistent, auditable investigation process. Its autonomous notebooks and tight observability integrations reduce toil, but it's not for teams without mature telemetry or those seeking a general-purpose AI assistant.
Verified 7d ago · liveness 55/100 · cite: rightaichoice.com/tools/relvy-ai
- SREs and on-call engineers dealing with high alert volumes
- Teams with existing runbooks wanting automated execution
- Platform teams standardizing incident response processes
- Organizations needing auditable investigation trails for compliance
- Teams without mature observability stack or runbooks
- Organizations not using modern incident management tools
- Engineers preferring manual, ad-hoc debugging over structured processes
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Skip Relvy if you don't have a mature observability stack (logs, metrics, traces) or if you're looking for a general-purpose AI assistant rather than a focused incident response tool.
Pricing is contact-sales only, so you'll need to negotiate; there's no self-serve tier to test before committing.
Relvy's pricing is not public—contact sales to get a quote. For teams already paying for PagerDuty and Datadog, the cost of Relvy may be incremental, but smaller teams might find it pricey compared to manual debugging or generic AI copilots.
In short
Relvy AI — Autonomous AI on-call engineer that investigates alerts and creates auditable notebooks. Best for SREs and on-call engineers dealing with high alert volumes, Teams with existing runbooks wanting automated execution, Platform teams standardizing incident response processes. Contact Sales pricing.
What people actually say about Relvy AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
4 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
- +Promises to automate repetitive runbook steps for on-call engineers.
- +Integrates with existing observability and incident management tools.
- +Structured investigation templates could standardize incident response.
- +AI copilot may reduce mean time to diagnosis (MTTD).
- +Post-incident exportable reports help with blameless post-mortems.
- −Zero independent user reviews or testimonials available publicly.
- −No evidence that AI suggestions are accurate or trustworthy.
- −Limited integration list; may not cover all monitoring tools teams use.
- −No free tier or trial to test before committing to sales process.
- −Unknown pricing may exclude small or mid-size engineering teams.
- • Unknown usage-based fees for AI queries or telemetry ingestion
Viability Score
How well maintained and how widely used is Relvy AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Autonomous alert investigation with AI agent
- Interactive investigation notebooks with visualizations
- Integration with telemetry, code, and infrastructure tools
- Runbook import and AI-assisted runbook creation
- Log analysis across multiple services
- Metrics and dashboard querying
- APM/trace analysis
- Code analysis from repositories
- Internal API support via MCP tools
- Continuous context layer with runbooks and incident memory
- SOC 2 Type II compliance
- Self-host deployment options
- REST API for automation
- Shared debugging sessions for team collaboration
- Structured post-mortem export
About Relvy AI
Relvy AI is an autonomous AI agent purpose-built for incident response. It takes over the repetitive work of investigating on-call alerts—running the same steps a senior engineer would across logs, metrics, traces, and code—and produces an auditable investigation notebook your team can review and trust. Brought to you by Y Combinator and SOC 2 Type II compliant, Relvy is designed for SREs, on-call engineers, and platform teams who want to reduce toil and standardize their debugging workflow. Instead of just suggesting fixes, Relvy actively investigates. It integrates with your existing telemetry, code, and infrastructure tools, pulling in a continuously updated context layer that includes runbooks, prior investigation memory, and live integrations. When an alert fires, Relvy digs through log lines, queries metrics dashboards, traces APM data, checks events and deployments, and inspects code repositories—all while building a shared debugging session your team can join in real time. The result is an interactive investigation notebook that captures every step, complete with rich visualizations. Your team can review the reasoning, add comments, and export a structured post-mortem when it's all over. Runbooks aren't a prerequisite either: Relvy supports plain-text runbook import and AI-assisted runbook creation, so you can build institutional knowledge as you go. It even exposes a REST API for automation, letting you wire investigations into your existing workflows. Relvy claims 70% of alerts are resolved in under 5 minutes, a figure that speaks to its focus on speed. Unlike generic AI copilots that require you to paste error logs into a chat, Relvy operates autonomously within your stack, standardizing the investigation process and leaving a clear audit trail. If your team is drowning in alerts and struggling to keep incident response consistent, Relvy offers a specialized, built-for-purpose alternative that prioritizes accountability and speed over general-purpose.
Behind the Verdict
Relvy fills a specific gap: it's an autonomous on-call engineer that does the grunt work of investigating alerts, rather than a chat copilot that waits for you to paste logs. The core value is the investigation notebook—an auditable trail of every step the agent took, which you can review, comment on, and export as a post-mortem. That's a compelling answer to the 'black box AI' problem in incident response. Strengths: autonomy (it actively queries logs, metrics, traces, and code instead of waiting for prompts), tight integrations with the observability stack (PagerDuty, New Relic, Datadog, Grafana, Splunk, AWS CloudWatch), and a memory layer that stores runbooks and past investigations so it gets smarter over time. The plain-text runbook import means you don't need to rewrite your existing processes. The REST API and MCP tool support make it scriptable, so you can bake investigations into your own workflows. Weaknesses: it's not a general-purpose assistant—if you want a chatbot or code generator, this isn't it. It depends heavily on having a mature observability setup; without good logs, metrics, and tracing, Relvy won't have much to work with. Pricing is opaque (contact sales), which can be a friction point for smaller teams. And like any AI tool, its accuracy depends on the quality of your incident data and how well it's trained on your stack. Where it fits: SRE teams drowning in alert volume, platform teams standardizing incident response, and orgs that need an audit trail for compliance. Where it doesn't: teams just starting with observability, or those who prefer manual ad-hoc debugging.
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Real-world workflow fit
Concrete scenarios for the personas Relvy AI actually fits — and what changes day-one when you adopt it.
You're on call and a PagerDuty alert fires at 2 AM for high error rate. Relvy picks it up automatically, queries Datadog, traces the error to a specific service, inspects the recent GitHub commits, and drafts a notebook with likely root cause.
Outcome: You wake up to a concise investigation summary with evidence, saving 30-60 minutes of manual digging.
You want to standardize incident response across your teams. You import your existing runbooks and enable AI-assisted runbook creation, so every alert investigation follows the same steps.
Outcome: Team members get consistent, auditable investigations, and you can track where the process breaks.
You need to prove to auditors how incidents are handled. After an incident, you export the structured post-mortem from Relvy, complete with every action taken and data queried.
Outcome: You have a clear audit trail that satisfies compliance requirements without extra effort.
Use Cases
- Debug a production incident by pulling logs, metrics, and traces into one notebook
- Use AI to generate SQL or query code to investigate suspicious patterns
- Collaborate with team members on a shared debugging session in real time
- Export an investigation as a structured post-mortem document
- Automate common debugging steps with the REST API
- Standardize incident response across teams by importing and executing runbooks
Limitations
- Pricing is not publicly available, and the platform likely requires a paid subscription beyond any free trial.
- Integration depth may vary, and support for some tools may be limited.
- The AI's effectiveness depends on the quality and quantity of incident data available for training.
as of 2026-08-07
Verification history
We have re-verified Relvy AI 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Relvy AI's pricing actually pencils out — and where peers do it cheaper.
Relvy's pricing is not public—contact sales to get a quote. For teams already paying for PagerDuty and Datadog, the cost of Relvy may be incremental, but smaller teams might find it pricey compared to manual debugging or generic AI copilots.
Setup time & first value
How long it actually takes to get something useful out of Relvy AI — broken out by persona, not the marketing-page minute.
Expect a few hours to a day to connect your telemetry and incident tools (PagerDuty, Datadog, etc.) and configure the context layer. The first investigation may take longer as the AI learns your stack, but you'll see value within the first few alerts.
Switching to or from Relvy AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual debugging: Start by importing your existing runbooks and let Relvy execute them automatically for new alerts.
- ↗To PagerDuty + manual: Export your investigation notebooks for historical reference, but note that automation will stop.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Relvy AI
Common stack mates teams adopt alongside Relvy AI, with the specific reason each pairing earns its keep.
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Featured Head-to-Head Comparisons
Relvy Ai vs Spider Cloud
Choose Spider Cloud if you need a high-speed, cost-effective API for crawling the web and feeding data into AI agents or RAG pipelines — its freemium model, 99.9% success rate, and new Browser AI commands make it a strong choice for developers. Choose Relvy AI if your team’s pain point is production incident response: its notebook-based debugging with AI copilot and seamless observability integrations are purpose-built for on-call engineers. They solve entirely different problems, so your decision hinges on whether you need external data extraction or internal system debugging tools.
Relvy Ai vs Temporal Ai
If you need to build fault-tolerant AI agents or orchestrate multi-step microservices that survive crashes, Temporal AI is the clear choice with its open-source durability, rich SDKs, and recent serverless workers. But if your pain point is debugging production incidents faster, Relvy AI offers a more focused, AI-powered notebook environment for on-call engineers. Choose based on your primary workflow: reliable execution vs. incident analysis.
Relvy Ai vs Voyage Ai
Voyage AI and Relvy AI serve completely different use cases: Voyage is for teams building high-accuracy RAG systems needing domain-specific embeddings and long-context support, while Relvy is for incident responders needing AI-assisted debugging notebooks with observability integrations. Choose based on your primary workflow — neither is a direct substitute.
Marvin vs Relvy Ai
Choose Marvin if you're a Python developer needing to add LLM smarts to your code with minimal fuss—it's free, open-source, and gets you from zero to AI-powered function in minutes. Choose Relvy AI if you're an SRE drowning in alerts and need an autonomous agent that investigates incidents using your existing observability stack, producing auditable notebooks. The tools solve completely different problems, so your choice hinges on whether you're building AI features or automating on-call response.
Alternatives to Relvy AI
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Frequently Asked Questions
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